Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [12]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
%matplotlib inline
import pickle
def calc_mtx_dst_and_save(root_dir,img_size):
if not os.path.isdir(root_dir):
print("The folder ",dir, "doesn't exist")
return
full_path = os.path.join(root_dir,'calibration*.jpg')
images = glob.glob(full_path)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = mpimg.imread(fname)
assert((img.shape[0]== img_size[1]) and (img.shape[1] ==img_size[0]))
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
full_pickle_path = os.path.join(root_dir,'camera_cal_pickle.p')
pickle.dump( dist_pickle, open(full_pickle_path, "wb" ) )
print("dump finished!")
def get_mtx_dst(root_dir):
full_pickle_path = os.path.join(root_dir,'camera_cal_pickle.p')
dist_pickle = {}
dist_pickle = pickle.load(open(full_pickle_path,mode= "rb" ) )
return dist_pickle["mtx"],dist_pickle["dist"]
Secondly Apply a distortion correction to raw images.¶
In [13]:
calc_mtx_dst_and_save("camera_cal",(1280,720))
mtx,dist = get_mtx_dst("camera_cal")
In [171]:
import matplotlib.image as mpimg
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
%matplotlib inline
def plot_images(images,titles,cmaps):
assert(len(images) > 1)
assert(len(images) == len(titles))
assert(len(images) == len(cmaps))
size = len(images)
f, ax = plt.subplots(1, size, figsize=(20,10))
for i,image,title,cmap in zip(range(size),images,titles,cmaps):
ax[i].imshow(image,cmap=cmap)
ax[i].set_title(title)
def get_perspective_transform():
img = mpimg.imread("test_images/straight_lines1.jpg")
src = np.float32([[203,700],[1076,720],[557,474],[723,474]])
img_size = (img.shape[1], img.shape[0])
original_X = 320
original_Y = 360
extend_X = 640
extend_Y = 360
dst = np.float32([[original_X, original_Y+extend_Y],[original_X +extend_X, original_Y+extend_Y],
[original_X, original_Y],
[original_X + extend_X, original_Y]])
M = cv2.getPerspectiveTransform(src, dst)
return M
M = get_perspective_transform()
def un_distor_demo():
images = []
titles = []
cmaps = []
img = mpimg.imread("test_images/test2.jpg")
undistor_img = cv2.undistort(img, mtx, dist, None, mtx)
images.append(img)
images.append(undistor_img)
titles.append("original")
titles.append("undistor")
cmaps.append("viridis")
cmaps.append("viridis")
plot_images(images,titles,cmaps)
un_distor_demo()
In [163]:
def abs_sobel_thresh(img, orient='x', thresh=(20,100)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return binary_output
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return the binary image
return binary_output
# Define a function to threshold an image for a given range and Sobel kernel
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
# Define a function that thresholds the S-channel of HLS
def hls_select(img, thresh=(170, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 1
return binary_output
def hls_sobel_thresh_select(image):
ksize = 3
hls_binary = hls_select(image, thresh=(20, 255))
gradx = abs_sobel_thresh(image, orient='x', thresh=(20, 100))
grady = abs_sobel_thresh(image, orient='y', thresh=(20, 100))
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(50, 100))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(0.7, 1.1))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
return combined
import numpy as np
import cv2
import matplotlib.pyplot as plt
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
def fine_lane(binary_warped):
histogram = np.sum(binary_warped[np.int(binary_warped.shape[0]/2):,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, 719, num=720)
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
right_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
# left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
# right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
# out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# plt.figure()
# plt.imshow(out_img)
# plt.plot(left_fitx, ploty, color='yellow')
# plt.plot(right_fitx, ploty, color='yellow')
# plt.xlim(0, 1280)
# plt.ylim(720, 0)
# plt.title(str(left_curverad) + " " + str(right_curverad))
return left_fit,right_fit,left_curverad,right_curverad
def plot_lane(img,left_fit,right_fit):
# Generate x and y values for plotting
ploty = np.linspace(0, img.shape[0]-1, img.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img = np.dstack((img, img, img))*255
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.figure()
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
In [161]:
def draw_result_back(color_image,binary_warped,left_fit,right_fit):
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
from numpy.linalg import inv
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, inv(M), (color_image.shape[1], color_image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(color_image, 1, newwarp, 0.3, 0)
return result
images = []
titles = []
cmaps = []
def select_region_of_interest(img,vertices):
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
def process_image(img):
img_size = (img.shape[1], img.shape[0])
undistor_img = cv2.undistort(img, mtx, dist, None, mtx)
image = undistor_img
hls_binary = hls_select(image, thresh=(90, 255))
hls_binary=select_region_of_interest(hls_binary,vertices)
gradx = abs_sobel_thresh(image, orient='x', thresh=(2, 100))
grady = abs_sobel_thresh(image, orient='y', thresh=(20, 100))
mag_binary = mag_thresh(image, sobel_kernel=15, mag_thresh=(30, 100))
dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(0.7, 1.0))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1) & (hls_binary ==1 ) |
(mag_binary == 1) & (dir_binary == 1) & (hls_binary ==1)) |
(gradx == 1) & (grady == 1) & (mag_binary == 1)
] = 1
warped = cv2.warpPerspective(combined, M, img_size)
left_fit,right_fit,left_curverad,right_curverad = fine_lane(np.array(warped))
final_result= draw_result_back(undistor_img,warped,left_fit,right_fit)
return final_result
for name in os.listdir("test_images"):
images.clear()
titles.clear()
cmaps.clear()
full_path = os.path.join("test_images",name)
img = mpimg.imread(full_path)
image = img
hls_binary = hls_select(image, thresh=(90, 255))
hls_binary=select_region_of_interest(hls_binary,vertices)
gradx = abs_sobel_thresh(image, orient='x', thresh=(2, 100))
grady = abs_sobel_thresh(image, orient='y', thresh=(20, 100))
mag_binary = mag_thresh(image, sobel_kernel=15, mag_thresh=(30, 100))
dir_binary = dir_threshold(image, sobel_kernel=15, thresh=(0.7, 1.0))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1) & (hls_binary ==1 ) |
(mag_binary == 1) & (dir_binary == 1) & (hls_binary ==1)) |
(gradx == 1) & (grady == 1) & (mag_binary == 1)
] = 1
images.append(img)
titles.append(name)
cmaps.append("viridis")
images.append(hls_binary)
titles.append("hls")
cmaps.append("gray")
images.append(gradx)
titles.append("gradx")
cmaps.append("gray")
plot_images(images,titles,cmaps)
images.clear()
titles.clear()
cmaps.clear()
images.append(grady)
titles.append("grady")
cmaps.append("gray")
images.append(mag_binary)
titles.append("mag")
cmaps.append("gray")
images.append(dir_binary)
titles.append("dir")
cmaps.append("gray")
plot_images(images,titles,cmaps)
images.clear()
titles.clear()
cmaps.clear()
images.append(combined)
titles.append("combine")
cmaps.append("gray")
img_size = (img.shape[1], img.shape[0])
M = get_perspective_transform()
warped = cv2.warpPerspective(img, M, img_size)
images.append(warped)
titles.append("warped_original")
cmaps.append("viridis")
warped = cv2.warpPerspective(combined, M, img_size)
images.append(warped)
titles.append("warped_filtered")
cmaps.append("gray")
plot_images(images,titles,cmaps)
left_fit,right_fit,left_curverad,right_curverad = fine_lane(np.array(warped))
final_result= draw_result_back(img,warped,left_fit,right_fit)
plt.figure()
plt.imshow(final_result)
# plot_lane(warped,left_fit,right_fit)
#plot_images(warped,titles,cmaps)
In [162]:
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
In [ ]:
In [158]:
import imageio
imageio.plugins.ffmpeg.download()
In [159]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
imageio.plugins.ffmpeg.download()
In [164]:
video_output = 'project_video_output.mp4'
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(video_output, audio=False)
In [ ]:
In [174]:
HTML("""
<video width="960" height="540" controls>
<source src="{video project_video_output.mp4}">
</video>
""".format(white_output))
In [ ]: